Search Results - (( problem implementation phase algorithm ) OR ( using classification using algorithm ))
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1
Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi
Published 2017“…In the implementation phase, Bubble Sort, Euclidean Distance, 10-Fold Cross validation, and K Nearest Neighbor algorithm are developed. …”
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2
Jogging activity recognition using k-NN algorithm
Published 2022“…The k-NN algorithm is a simple and easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. …”
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3
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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4
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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5
Sentiment analysis for malay newspaper (SAMNews) using negative selection algorithm / Nur Amalina Redzuan
Published 2013“…In future, a comparative study on Artificial Immune System and other techniques or algorithms can be carried out to enhance the performance of the classification model.…”
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6
An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2013“…Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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7
An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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8
An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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9
Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
Published 2014“…Due to that reason, this study attempts to use SVM algorithm on employee’s performance databases for talent classification. …”
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10
Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network
Published 2024“…This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. …”
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. …”
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Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
Published 2024“…The implementation phase focuses on the deployment of the Decision Tree algorithm and system evaluation through functionality testing. …”
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13
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine
Published 2024“…Despite using the legacy classifier algorithm, an improvement in dataset management presented in this paper proves that it is still realistic to implement and does not require demanding computational power to achieve a good classification result. …”
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Security alert framework using dynamic tweet-based features for phishing detection on twitter
Published 2019“…This framework is divided into three phases which are classification model of phishing detection, detection algorithm of phishing tweet detection and security alert mechanism of phishing tweet detection. …”
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15
Festive season balancing groceries optimization / Fairuz Mohamed Razi
Published 2012“…An Artificial Intelligence method or technique is selected to provide a solution to this problem. The cause of the shortage in supply-demand is taken to be implemented in the Genetic Algorithms optimization concepts. …”
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16
Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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17
Drone-based surveillance of palm tress ecosystems
Published 2024“…The study aims to improve the accuracy and efficiency of palm health detection by integrating MATLAB's initial object recognition with advanced deep learning algorithms. The initial phase of the research focuses on elucidating the challenges associated with detecting palm tree health issues using conventional image processing methods in MATLAB. …”
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Multi-objective optimisation of assembly line balancing type-e problem with resource constraints
Published 2016“…In the second phase of this research, an algorithm will be developed to optimise the problem. …”
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19
An enhanced Blowfish Algorithm based on cylindrical coordinate system and dynamic permutation box
Published 2014“…Thus, the objective of this research is to enhance the BA to overcome these problems. The research involved three phases, algorithm design, implementation, and evaluation. …”
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20
Enhancement of stemming process for malay illicit web content
Published 2012“…As conclusion, the implementation of new features in enhanced stemming algorithm can reduce the errors produce in Sembok’s stemming algorithm. …”
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